Building Scalable Data Pipelines on AWS: Lessons Learned

Building Scalable Data Pipelines on AWS: Lessons Learned

Over the past few years working with BMW Group, I’ve had the opportunity to work on several large-scale data engineering projects. In this post, I’ll share some key insights and lessons learned from building data pipelines on AWS.

The Challenge

When dealing with vehicle telemetry data, we faced several challenges:

• Processing massive amounts of real-time data
• Ensuring data quality and consistency
• Optimizing costs while maintaining performance
• Building maintainable and scalable systems

Key Solutions

1. Apache Iceberg for Data Lake Management

One of the most impactful decisions we made was implementing Apache Iceberg. This helped us:

• Optimize query performance
• Manage schema evolution
• Improve data lake metadata handling

2. Real-time Processing with Apache Kafka

Using Kafka allowed us to:

• Handle high-throughput data streams
• Ensure reliable message delivery
• Decouple data producers and consumers

Lessons Learned

1. Start with good data modeling
2. Invest in monitoring and observability
3. Consider cost implications early
4. Build for maintainability

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